Methods of Forecasting Electric Energy Consumption: A Literature Review

Roman V. Klyuev, Irbek D. Morgoev, Angelika D. Morgoeva (), Oksana A. Gavrina, Nikita V. Martyushev, Egor A. Efremenkov and Qi Mengxu
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Roman V. Klyuev: Technique and Technology of Mining and Oil and Gas Production Department, Moscow Polytechnic University, 33, B. Semenovskaya Str., 107023 Moscow, Russia
Irbek D. Morgoev: Department of Information Technologies and Systems, North Caucasian Institute of Mining and Metallurgy (State Technological University), 44, Nikolaev Str., 362011 Vladikavkaz, Russia
Angelika D. Morgoeva: Department of Information Technologies and Systems, North Caucasian Institute of Mining and Metallurgy (State Technological University), 44, Nikolaev Str., 362011 Vladikavkaz, Russia
Oksana A. Gavrina: Department of Industrial Power Supply, North Caucasian Institute of Mining and Metallurgy (State Technological University), 44, Nikolaev Str., 362011 Vladikavkaz, Russia
Nikita V. Martyushev: Department of Advanced Technologies, Tomsk Polytechnic University, 30, Lenin Ave., 634050 Tomsk, Russia
Egor A. Efremenkov: Department of Advanced Technologies, Tomsk Polytechnic University, 30, Lenin Ave., 634050 Tomsk, Russia
Qi Mengxu: Department of Advanced Technologies, Tomsk Polytechnic University, 30, Lenin Ave., 634050 Tomsk, Russia

Energies, 2022, vol. 15, issue 23, 1-33

Abstract: Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).

Keywords: forecasting; power consumption; modeling; energy saving; machine learning; deep learning; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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